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Welcome!

This website provides interactive results for the forecasting models explored in the paper Estimating Neighborhood Asking Rents using Scraped Craigslist Rental Listings.

The goal of this research is to a.) understand the temporal dynamics of rent estimates in Seattle and b.) forecast the current quarter’s rent levels based off of the prior periods. The focal series of models regress median one-bedroom rent asked values on different specifications of the panel’s correlation structure (i.e. temporal and spatial). All of the candidate models’ posterior distributions are estimated with integrated nested Laplace approximations (INLA) using the default, weakly-informative priors for all model hyperparameters. Throughout the following analyses, the training data are 2017 Q2 up to the prior quarter (i.e. 2018 Q1). The test period is a forecast for the current period and includes comparison to the appropriate median rent estimates for data observed so far in this period.

Most graphics include some level of interactivity, usually either hover-over tooltip information or a slider to control various views of the graphic. Clicking on cases will highlight data elements in most graphics, and double-clicking will reset the graphic. You can download the source code and data for this project from Github here.

Contact Chris Hess at hesscl@uw.edu for more information about this research.

This page was last updated: 2018-05-30




Table of Contents

Page Description
Distribution density graphic to investigate the distribution of rents among Seattle neighborhoods for each quarter
Panel Time-Series line graphic to show the observed or modeled temporal structure
Spatial Time-Series series of maps to show observed change across time
Model Fit tables of model root mean square error (RMSE), mean absolute error (MAE), and deviance information criterion (DIC) across training and test data

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Observed vs. Smoothed Rent Estimates

Distribution

Panel Time-Series

Spatial Time-Series

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Observed

Non-Spatial AR(1)

Spatial AR(1)

Spatiotemporal AR(1)

Model Fit

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Model Legend

Model abbr. Description
int Quarter fixed intercept
log(med1B) ~ 1 + Qtr
ns Non-spatial tract random effect for each tract, quarter fixed intercept
log(med1B) ~ 1 + Qtr + f(idtract, model = “iid”)
nsar1 Non-spatial tract random effect, AR(1) random effect for prior quarter, i.i.d random effect for current quarter
log(med1B) ~ 1 + f(idtract, model = “iid”) + f(idqtr, model = “ar1”) + , f(idqtr1, model = “iid”)
bym Spatial intrinsic conditional autoregressive (ICAR) tract random-effect, non-spatial i.i.d tract random effect, AR(1) random effect for prior quarter, i.i.d random effect for current quarter
log(med1B) ~ 1 + f(idtract, model = “bym”, scale.model = T, graph = “./output/seatract.graph”) + , f(idqtr, model = “ar1”) + f(idqtr1, model = “iid”)
spt Spatial intrinsic conditional autoregressive (ICAR) tract random-effect, non-spatial i.i.d tract random effect, AR(1) random effect for prior quarter, i.i.d random effect for current quarter, i.i.d. random effect for tract-quarter (space-time interaction)
log(med1B) ~ 1 + f(idtract, model = “bym”, scale.model = T, graph = “./output/seatract.graph”) + , f(idqtr, model = “ar1”) + f(idqtr1, model = “iid”) + f(idtractqtr, , model = “iid”)

Accuracy and Information Criteria

train_test int_rmse ns_rmse nsar1_rmse bym_rmse spt_rmse
Test 309.9796 219.0896 210.9084 203.8999 226.56503
Training 324.3490 136.1696 136.9117 138.9922 59.91497



train_test int_mae ns_mae nsar1_mae bym_mae spt_mae
Test 254.9985 161.45414 152.23670 147.5961 163.25741
Training 258.1041 89.43892 90.29539 93.7161 40.05511



train_test int_DIC ns_DIC nsar1_DIC bym_DIC spt_DIC
Training -168.1242 -679.9016 -679.7958 -685.4692 -913.2261



train_test int_WAIC ns_WAIC nsar1_WAIC bym_WAIC spt_WAIC
Training -167.4504 -661.0794 -660.4344 -667.959 -907.6226

Hyperparameters

Non-Spatial AR(1)
mean sd 0.025quant 0.5quant 0.975quant mode
Precision for the Gaussian observations 88.0541 6.6442 75.6237 87.8362 101.7756 87.4460
Precision for idtract 29.8512 4.1501 22.4418 29.5975 38.7549 29.1277
Precision for idqtr 7093.9982 12771.7425 518.2153 3528.3813 35685.9016 1263.1158
Rho for idqtr 0.3063 0.3980 -0.5647 0.3682 0.8880 0.6178
Precision for idqtr1 20111.6959 28250.3371 534.8344 10969.1455 94067.7521 1019.0632



Spatial AR(1)
mean sd 0.025quant 0.5quant 0.975quant mode
Precision for the Gaussian observations 87.6624 6.6352 75.1579 87.4841 101.2625 87.2215
Precision for idtract (iid component) 105.7944 29.9038 59.1067 101.7891 175.7562 94.2656
Precision for idtract (spatial component) 76.2521 23.3966 40.2309 72.9681 131.4461 66.8234
Precision for idqtr 5106.1606 7975.6103 370.1895 2773.4934 24254.5455 951.2852
Rho for idqtr 0.3219 0.4132 -0.5897 0.3937 0.9068 0.6851
Precision for idqtr1 22549.1734 28007.8723 1109.7363 13834.7710 96641.5261 2777.2088



Spatiotemporal AR(1)
mean sd 0.025quant 0.5quant 0.975quant mode
Precision for the Gaussian observations 207.9086 34.6615 146.2087 203.5460 297.4634 197.3851
Precision for idtract (iid component) 104.4310 29.2694 59.0432 100.3640 172.9769 92.7440
Precision for idtract (spatial component) 76.8144 23.5799 40.1189 73.6553 131.9520 67.6963
Precision for idqtr 5790.2578 9798.2957 413.9060 2991.4206 28584.8204 1037.9941
Rho for idqtr 0.3122 0.4105 -0.5919 0.3821 0.8983 0.6595
Precision for idqtr1 22288.6149 29696.4042 882.8369 12951.8650 100218.9668 2075.5769
Precision for idtractqtr 153.9790 19.6393 115.3368 152.7407 202.6121 152.3793

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Non-Spatial AR(1)

Spatial AR(1)

Spatiotemporal AR(1)